雾天降质图像的增强复原算法研究
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摘要
图像增强和图像复原是为了突出图像中某些细节信息,以利于人眼的视觉观察或计算机后续分析处理。雾霾天气,由于大气粒子的作用,获取的图像对比度一般较低,影响了它的应用,使得现有视频监控、目标跟踪实际应用等对天气非常敏感。
为提高系统的鲁棒性,国内外众多研究人员分别从图像增强和图像复原两个方面对雾天图像清晰化进行了广泛的研究,取得了许多研究成果,由于本身问题的复杂性,现有算法都不能够很好的解决此清晰化问题,因此对雾天退化图像进行有效处理具有重要的理论和实际意义。
基于上述研究背景,本文详细分析了雾天图像的降质机理,致力于对现有一些算法改进、完善,以及在新方法引入等方面做了一些研究工作,本文主要的贡献归纳如下：
(1)提出了一种改进的Retinex图像增强算法。克服了Retinex增强方法中传统的中心/环绕函数在处理快速变化的范围内,明暗对比处产生的光晕现象,提高了图像的对比度并很好的保持物体的颜色。通过引入各项异性滤波器设计方法,对多尺度Retinex增强算法进行了改进,由像素点处的灰度梯度确定各向异性滤波器长轴方向。给出了具体算法流程,并就实际雾天图像进行了增强仿真实验,结果证明了该方法的有效性。
(2)提出了基于人眼视觉特性的雾天图像对比度增强方法。介绍了带参数的对数图像处理,算法利用人眼视觉特性将雾天降质图像的亮度分量分割成为几个特性相似的区域：德弗里斯区域、韦伯区域、饱和区域以及低对比度区域,然后对各子图像采用改进的对比度受限自适应直方图均衡增强后比例融合。最后给出了改进的算法流程,仿真实验验证算法可以增强雾天图像的对比度和细节,验证算法的有效性。
(3)提出了单幅图像的复原算法。分析了大气衰减物理模型,介绍了传统的使用多幅不同雾况下完全相同场景下的图像复原方法,该方法可以达到较好的处理效果,但如此严格输入要求限制了算法的使用,特别是在实时应用中。通过引入暗原色先验信息,结合雾天图像的退化模型,对基于暗原色的单幅图像去雾算法进行了改进。通过对包含天空区域的图像进行自动分割,由分割得到的天空区域最亮像素的10%的均值作为大气光的估计,在图像不包含天空区域或天空区域难以自动分割的情况下,利用传输图等信息,推导出大气光的估计,以提高大气光估计的精确性。给出了改进算法实现的主要步骤和一系列的雾天降质图像的复原仿真实验,结果比较分析表明了所提出算法的有效性。
论文对雾天降质图像清晰化算法进行研究与探讨,并通过几种不同方法对原有算法进行改进,几组不同图像清晰化仿真实验结果验证了论文中提出算法的有效性。
Image enhancement and image restoration aim at highlighting some details in the original image for human vision or computer analyzing and processing. In foggy weather, the contrast of images is drastically degraded mainly due to the presence of considerable number of atmospheric particles such as dust, mist and fumes, which makes some applications such as video surveillance, target tracking very sensitive to weather conditions.
Many scientists have researched the clearness technique in bad weather conditions from two aspects including image enhancement and image restoration to improve the system robustness. Some achievements have been made in the past years. But the problem is so complicated and difficult that the current algorithms cannot work it well. There is still much work to do on it, so it has a great significance to study how to enhance or restore the fog-degraded image more efficiently.
For these reasons, this dissertation mainly focuses on the analysis of image degradation mechanisms and the modification of some current algorithms and techniques to tackle the problem more effectively. In summary, our meaningful and detailed research works of this thesis are listed as follows:
(1) To overcome the halo artifacts in the region where the illumination changes rapidly, which the traditional center/surround-based Retinex enhancement algorithm often suffers from, a modified Multi-Scale Retinex color image enhancement algorithm is proposed. The modified algorithm is more effective than the MSR algorithm in improving the contrast and preserving the color of the original image. In the modified algorithm, an adaptive anisotropic Gaussian filtering method is introduced, and the orientation of the long axes is determined according to the gradient orientation in the position. The modified algorithm is given and applied in the fog-degraded images enhancement. Finally, the experimental result comparison with the previous methods testified the validity of the modified algorithm.
(2) A fog-degraded image enhancement method based on human visual system (HVS) is proposed. The parameterized logarithmic image processing (PLIP) model is introduced. The proposed algorithm utilizes the human visual system to segment the intensity components of the fog-degraded image into Devries-Rose region, Weber region, low-contrast and saturation region sub-images. With the modified contrast limited adaptive histogram equalization (CLAHE), the sub-images can be enhanced and fused to scale. The flow chart for the human-vision-based contrast enhancement algorithm is given. The defog experiments have been done to illustrate that the method can enhancement the contrast and details of fog-degraded images efficiently.
(3) A single image restoration method to remove haze is presented. Atmospheric degraded model is analyzed. Using multiple images taken from foggy scenes with different densities, the traditional method can remove weather effects and produce reasonably good results. But the method requires that the images are taken from exactly the same point of view. The input requirement makes the method impractical, particularly in real time applications. The Dark Channel Prior (DCP) and the image restoration algorithm using DCP from a single input image are introduced. We modified the algorithm to improve the precision of atmospheric light estimation. The sky region is separated from the degraded image and the value of atmospheric light is estimation by the average of the 10% brightest pixels in the sky region. If the original image don't contain the sky region or the sky region can't be separated accurately, the value of atmospheric light can be derived from the atmospheric degraded model by the transmission map. The procedure of the proposed algorithm is presented. Some experimental results of fog removal and the comparison between different algorithms testified the validity of the proposed algorithm.
In a word, the clearness algorithms is studied and explored in this dissertation, the traditional methods is modified and improved by several means. The experimental results on different images show the efficiency of the proposed algorithms.
为提高系统的鲁棒性,国内外众多研究人员分别从图像增强和图像复原两个方面对雾天图像清晰化进行了广泛的研究,取得了许多研究成果,由于本身问题的复杂性,现有算法都不能够很好的解决此清晰化问题,因此对雾天退化图像进行有效处理具有重要的理论和实际意义。
基于上述研究背景,本文详细分析了雾天图像的降质机理,致力于对现有一些算法改进、完善,以及在新方法引入等方面做了一些研究工作,本文主要的贡献归纳如下：
(1)提出了一种改进的Retinex图像增强算法。克服了Retinex增强方法中传统的中心/环绕函数在处理快速变化的范围内,明暗对比处产生的光晕现象,提高了图像的对比度并很好的保持物体的颜色。通过引入各项异性滤波器设计方法,对多尺度Retinex增强算法进行了改进,由像素点处的灰度梯度确定各向异性滤波器长轴方向。给出了具体算法流程,并就实际雾天图像进行了增强仿真实验,结果证明了该方法的有效性。
(2)提出了基于人眼视觉特性的雾天图像对比度增强方法。介绍了带参数的对数图像处理,算法利用人眼视觉特性将雾天降质图像的亮度分量分割成为几个特性相似的区域：德弗里斯区域、韦伯区域、饱和区域以及低对比度区域,然后对各子图像采用改进的对比度受限自适应直方图均衡增强后比例融合。最后给出了改进的算法流程,仿真实验验证算法可以增强雾天图像的对比度和细节,验证算法的有效性。
(3)提出了单幅图像的复原算法。分析了大气衰减物理模型,介绍了传统的使用多幅不同雾况下完全相同场景下的图像复原方法,该方法可以达到较好的处理效果,但如此严格输入要求限制了算法的使用,特别是在实时应用中。通过引入暗原色先验信息,结合雾天图像的退化模型,对基于暗原色的单幅图像去雾算法进行了改进。通过对包含天空区域的图像进行自动分割,由分割得到的天空区域最亮像素的10%的均值作为大气光的估计,在图像不包含天空区域或天空区域难以自动分割的情况下,利用传输图等信息,推导出大气光的估计,以提高大气光估计的精确性。给出了改进算法实现的主要步骤和一系列的雾天降质图像的复原仿真实验,结果比较分析表明了所提出算法的有效性。
论文对雾天降质图像清晰化算法进行研究与探讨,并通过几种不同方法对原有算法进行改进,几组不同图像清晰化仿真实验结果验证了论文中提出算法的有效性。
Image enhancement and image restoration aim at highlighting some details in the original image for human vision or computer analyzing and processing. In foggy weather, the contrast of images is drastically degraded mainly due to the presence of considerable number of atmospheric particles such as dust, mist and fumes, which makes some applications such as video surveillance, target tracking very sensitive to weather conditions.
Many scientists have researched the clearness technique in bad weather conditions from two aspects including image enhancement and image restoration to improve the system robustness. Some achievements have been made in the past years. But the problem is so complicated and difficult that the current algorithms cannot work it well. There is still much work to do on it, so it has a great significance to study how to enhance or restore the fog-degraded image more efficiently.
For these reasons, this dissertation mainly focuses on the analysis of image degradation mechanisms and the modification of some current algorithms and techniques to tackle the problem more effectively. In summary, our meaningful and detailed research works of this thesis are listed as follows:
(1) To overcome the halo artifacts in the region where the illumination changes rapidly, which the traditional center/surround-based Retinex enhancement algorithm often suffers from, a modified Multi-Scale Retinex color image enhancement algorithm is proposed. The modified algorithm is more effective than the MSR algorithm in improving the contrast and preserving the color of the original image. In the modified algorithm, an adaptive anisotropic Gaussian filtering method is introduced, and the orientation of the long axes is determined according to the gradient orientation in the position. The modified algorithm is given and applied in the fog-degraded images enhancement. Finally, the experimental result comparison with the previous methods testified the validity of the modified algorithm.
(2) A fog-degraded image enhancement method based on human visual system (HVS) is proposed. The parameterized logarithmic image processing (PLIP) model is introduced. The proposed algorithm utilizes the human visual system to segment the intensity components of the fog-degraded image into Devries-Rose region, Weber region, low-contrast and saturation region sub-images. With the modified contrast limited adaptive histogram equalization (CLAHE), the sub-images can be enhanced and fused to scale. The flow chart for the human-vision-based contrast enhancement algorithm is given. The defog experiments have been done to illustrate that the method can enhancement the contrast and details of fog-degraded images efficiently.
(3) A single image restoration method to remove haze is presented. Atmospheric degraded model is analyzed. Using multiple images taken from foggy scenes with different densities, the traditional method can remove weather effects and produce reasonably good results. But the method requires that the images are taken from exactly the same point of view. The input requirement makes the method impractical, particularly in real time applications. The Dark Channel Prior (DCP) and the image restoration algorithm using DCP from a single input image are introduced. We modified the algorithm to improve the precision of atmospheric light estimation. The sky region is separated from the degraded image and the value of atmospheric light is estimation by the average of the 10% brightest pixels in the sky region. If the original image don't contain the sky region or the sky region can't be separated accurately, the value of atmospheric light can be derived from the atmospheric degraded model by the transmission map. The procedure of the proposed algorithm is presented. Some experimental results of fog removal and the comparison between different algorithms testified the validity of the proposed algorithm.
In a word, the clearness algorithms is studied and explored in this dissertation, the traditional methods is modified and improved by several means. The experimental results on different images show the efficiency of the proposed algorithms.
引文
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